arXiv subjects

Duy Cao

Publications and source records attributed to Duy Cao.

3 recordsLinked to original sources

arXiv2026

HxAgent: Iterative Agent Planning for End-to-End Web Application Testing

In automated web testing, generating test cases and performing testing using functionality descriptions in natural-language is crucial for improving efficacy. These tasks require such a testing agent to carry out tasks on the target application and generating tests autonomously. We introduce HxAgent, an iterative LLM-based planning agent with a proactive correction strategy. After each step, HxAgent reassesses the web state to determine the next action using (1) current observations, (2) short-term memory of past actions, and (3) long-term experience extracted from past (in)correct sequences of actions. HxAgent achieves 97.4% Exact-Match accuracy on MiniWoB++, comparable to the best baselines without human demonstrations and surpassing the recent WALT by 10.5%. On a dataset of 350 web tasks, it attains 83.8% Exact-Match and 91.8% Prefix-Match, exceeding WALT by 13.4%. On OnlineMind2Web, it further improves over WALT by 4.6%.

cs.SE↗

arXiv2025

Toward Generation of Test Cases from Task Descriptions via History-aware Planning

In automated web testing, generating test scripts from natural language task descriptions is crucial for enhancing the test generation process. This activity involves creating the correct sequences of actions to form test scripts for future testing activities. Current state-of-the-art approaches are limited in generating these action sequences, as they either demand substantial manual effort for human demonstrations or fail to consider the history of previous web content and actions to decide the next action. In this paper, we introduce HxAgent, an iterative large language model agent planning approach that determines the next action based on: 1) observations of the current contents and feasible actions, 2) short-term memory of previous web states and actions, and 3) long-term experience with (in)correct action sequences. The agent generates a sequence of actions to perform a given task, which is effectively an automated test case to verify the task. We conducted an extensive empirical evaluation of HxAgent using two datasets. On the MiniWoB++ dataset, our approach achieves 97% exact-match accuracy that is comparable to the best baselines while eliminating the need for human demonstrations required by those methods. For complex tasks requiring navigation through multiple actions and screens, HxAgent achieves an average 82% exact-match. On the second dataset, comprising 350 task instances across seven popular websites, including YouTube, LinkedIn, Facebook, and Google, HxAgent achieves high performance, with 87% of the action sequences exactly matching the ground truth and a prefix-match of 93%, outperforming the baseline by 59%.

cs.SE↗

arXiv2024

KAT: Dependency-aware Automated API Testing with Large Language Models

API testing has increasing demands for software companies. Prior API testing tools were aware of certain types of dependencies that needed to be concise between operations and parameters. However, their approaches, which are mostly done manually or using heuristic-based algorithms, have limitations due to the complexity of these dependencies. In this paper, we present KAT (Katalon API Testing), a novel AI-driven approach that leverages the large language model GPT in conjunction with advanced prompting techniques to autonomously generate test cases to validate RESTful APIs. Our comprehensive strategy encompasses various processes to construct an operation dependency graph from an OpenAPI specification and to generate test scripts, constraint validation scripts, test cases, and test data. Our evaluation of KAT using 12 real-world RESTful services shows that it can improve test coverage, detect more undocumented status codes, and reduce false positives in these services in comparison with a state-of-the-art automated test generation tool. These results indicate the effectiveness of using the large language model for generating test scripts and data for API testing.

cs.SE↗